CN112837799A - Remote internet big data intelligent medical system based on block chain - Google Patents

Remote internet big data intelligent medical system based on block chain Download PDF

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CN112837799A
CN112837799A CN202110125345.XA CN202110125345A CN112837799A CN 112837799 A CN112837799 A CN 112837799A CN 202110125345 A CN202110125345 A CN 202110125345A CN 112837799 A CN112837799 A CN 112837799A
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曹茂诚
王洪平
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Guangdong Deao Smart Medical Technology Co ltd
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    • G—PHYSICS
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Abstract

Remote internet big data wisdom medical system based on block chain, including vital sign acquisition module, medical treatment big data acquisition module, block chain storage module and wisdom medical terminal, establish the health assessment model that carries out the aassessment according to vital sign data according to the vital sign big data establishment that medical treatment big data acquisition module collected, the patient's that will gather vital sign acquisition module vital sign data input health assessment model in to the health assessment model, thereby it is healthy or dangerous to assess the physical condition of patient, and early warning when dangerous. The invention has the beneficial effects that: the remote monitoring system has the advantages that the remote unified monitoring of the physical state of the patient is realized, and when the physical state of the patient is dangerous, early warning can be timely carried out.

Description

Remote internet big data intelligent medical system based on block chain
Technical Field
The invention relates to the field of intelligent medical treatment, in particular to a remote internet big data intelligent medical treatment system based on a block chain.
Background
With the enhancement of health consciousness and social aging of people, more and more patients in hospitals are provided, and the enhancement of right maintenance consciousness of people, nurses generally feel large working pressure and heavy work tasks, and 90% of all treatment required by one patient from admission to discharge is completed by the nurses; 90.42% of nurses have work time of more than 40 hours every week, 74.2% of nurses have the condition of night shift, because nursing hospital's nursing gap is big, lead to the fact the threshold of inviting labour to constantly reduce, generally have at present to learn to be low, the condition of lacking professional nursing skill, under the heavy operating pressure, the responsibility is lack of mind or careless results in relying on manpower alone 24 hours to nurse the degree of difficulty very big continuously.
Aiming at the situation, in order to reduce the workload of nurses, the invention provides the remote internet big data intelligent medical system based on the block chain.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a remote internet big data intelligent medical system based on a blockchain.
The purpose of the invention is realized by the following technical scheme:
the remote Internet big data intelligent medical system based on the blockchain comprises a vital sign acquisition module, a medical big data acquisition module, a blockchain storage module and an intelligent medical terminal, wherein the vital sign acquisition module is bound with a bed number of a patient and used for acquiring vital sign data of the patient and transmitting the acquired vital sign data and the bed number of the patient to the intelligent medical terminal through the Internet, the medical big data acquisition module is used for collecting the vital sign big data and transmitting the collected vital sign big data to the blockchain storage module for storage, the intelligent medical terminal comprises a big data processing unit, a vital sign analysis unit, an intelligent early warning unit, a patient information recording unit and a human-computer interaction unit, the intelligent medical terminal retrieves the vital sign big data from the blockchain storage module and inputs the retrieved vital sign big data to the big data processing unit for processing, the vital sign analysis unit establishes a health assessment model for assessing the body state of a patient according to vital sign data according to the processed vital sign big data, and inputs the received vital sign data of the patient into the health assessment model, so as to assess whether the body state of the patient is healthy or dangerous, when the body state of the patient is dangerous, the intelligent early warning unit gives an early warning, the patient information recording unit is used for recording the basic information of the patient, the vital sign data of the patient and the body state of the patient obtained by assessment, and medical staff can inquire the vital sign data of the patient and the body state of the patient by inputting the basic information of the patient into the man-machine interaction unit.
Preferably, the vital sign analysis unit adopts a support vector machine to establish a health assessment model for assessing the body state according to the vital sign data, and adopts the vital sign big data processed by the big data processing unit as a sample set for training and testing the support vector machine.
Preferably, the basic information of the patient includes a name, an age, and a bed number of the patient.
Preferably, the big data processing unit is configured to cluster the vital sign big data, remove noise data in the vital sign big data in a clustering process, determine labels of body states corresponding to various class sets obtained by clustering the vital sign big data, and in a training process of the support vector machine, use the class set of the vital sign big data as an input value of the support vector machine, and use the label of the body state corresponding to the class set as an output value of the support vector machine.
Preferably, the physical status label includes health and risk.
Preferably, the big data processing unit is configured to cluster the vital sign big data and remove noise data in the vital sign big data in a clustering process, and specifically includes:
(1) selecting a class center from the vital sign big data;
(2) and clustering the data in the vital sign big data according to the selected class center, and removing noise data in the vital sign big data in the clustering process.
Preferably, the class center is selected from the vital sign big data in the following way:
let Y denote the set of vital sign big data, YiRepresents the ith data in set Y, defines s (Y)i) Representing data yiGlobal similarity coefficient in set Y, and
Figure BDA0002923805680000021
wherein, yjRepresents the jth data in the set Y, and M (Y) represents the number of data in the set Y; is provided with U (y)i) Representing data yiGiven a positive integer M, M data are selected from the set Y and added into the set U (Y)i) The method comprises the following steps:
let yi(1) Representing distance data Y in a set YiFirst near data, ω (y)i(1) Represents data y)i(1) And ω (y) ofi(1) Is based on data
Figure BDA0002923805680000022
Centered at | yi(1)-yiL is a square region with side length, and data yi(1) Join to set U (y)i) And in the local region ω (y)i(1) Determine distance data y from the extrinsic dataiThe first most recent data is denoted as yi(2) Let ω (y)i(2) Represents data y)i(2) And ω (y) ofi(2) Is based on data
Figure BDA0002923805680000023
Centered at | yi(2)-yiL is a square region with side length, and data yi(2) Join to set U (y)i) In the local region ω (y)i(1) And a local region ω (y)i(2) Determine distance data y from the extrinsic dataiThe first most recent data is denoted as yi(3) Let ω (y)i(3) Represents data y)i(3) Part ofRegion, and ω (y)i(3) Is based on data
Figure BDA0002923805680000031
Centered at | yi(3)-yiL is a square region with side length, and data yi(3) Join to set U (y)i) And continuing to determine the addition of data to the set U (y) as described abovei) In, up to set U (y)i) When the number of data in the set is equal to M, stopping the direction to the set U (y)i) Adding data;
candidate data which can be used as class centers are screened out from the set Y by adopting the following formula:
Figure BDA0002923805680000032
wherein f (y)i) Representing data yiClass-centric attribute value in set Y, Yi(l) A set of representations U (y)i) The first data in (1), s (y)i(l) Represents data y)i(l) Global similarity coefficient in set Y, ρ (Y)i(l)，yi) Representing data yi(l) Compare to data yiA distance weighted value of, and
Figure BDA0002923805680000033
when data yiClass center attribute value of
Figure BDA0002923805680000034
Then data y is addediThe candidate data of the class center is judged to be non-noise data; when data yiClass center attribute value of
Figure BDA0002923805680000035
Then data y is addediThe data are regarded as non-clustered data;
setting L (Y) to represent a set formed by candidate data regarded as class centers in the set Y, selecting the class centers in the set L (Y), and clustering the candidate data in the set L (Y) according to the selected class centers, wherein the method specifically comprises the following steps:
selecting the candidate data with the maximum global similarity coefficient in the set L (Y) as a first class center, and marking the first class center as c1Class center c1The class set is marked as C1Centering the class c1Deleted in the set L (Y), and screened out from the current set L (Y) as belonging to the class set C by the following steps1The other candidate data of (2) are specifically:
step (1): let lk(1) The k-th candidate data in the set L (Y) at the time of the 1 st screening is shown, and G (l) is definedk(1)，C1) Represents the candidate data lk(1) And class set C1A clustering function between, and G (l)k(1)，C1) The expression of (a) is:
G(lk(1)，C1)＝θ(lk(1)，C1)*|s(lk(1))-s(c1)|
in the formula, theta (l)k(1)，C1) Representing a judgment function, set N (l)k(1) ) represents the distance candidates l selected in the set Yk(1) Neighborhood set of M nearest data, M (l)k(1)，C1) Representing a neighborhood set N (l)k(1) In) to the class set C1Number of data of (1), when m (l)k(1)，C1) Not equal to 0, θ (l)k(1)，C1) When m (l) is equal to 1k(1)，C1) When equal to 0, θ (l)k(1)，C1)＝0，s(c1) Representing class center c1Global similarity coefficient in set Y, s (l)k(1) Represents candidate data lk(1) Global similarity coefficients in set Y;
when in use
Figure BDA0002923805680000041
Then, the candidate data l is determinedk(1) As class set C1The candidate data lk(1) Adding to class collections C1And the candidate data lk(1) Deleted in the set L (Y) when G (l)k(1)，C1) 0 or
Figure BDA0002923805680000042
Then the candidate data lk(1) Reserved in the set L (Y);
step (2) settingK(2) Representing the Kth candidate data in the current set L (Y) at the 2 nd screening, and defining G (l)K(2)，C1) Represents the candidate data lK(2) And class set C1A clustering function between, and G (l)K(2)，C1) The expression of (a) is:
Figure BDA0002923805680000043
wherein, θ (l)K(2)，C1) Representing a judgment function, set N (l)K(2) Means for selecting distance candidates l from the set YK(2) Neighborhood set of M nearest data, M (l)K(2)，C1) Representing a neighborhood set N (l)K(2) In) to the class set C1Number of data of (1), when m (l)K(2)，C1) Not equal to 0, θ (l)K(2)，C1) When m (l) is equal to 1K(2)，C1) When equal to 0, θ (l)K(2)，C1)＝0，s(lK(2) Represents candidate data lK(2) Global similarity coefficient in set Y, Y1，zRepresenting class set C1Is the z-th data in (1), ρ (y)1，z，lK(2) Represents data y)1，zCompared with the candidate data lK(2) A distance weighted value of, and
Figure BDA0002923805680000044
Figure BDA0002923805680000045
s(y1，z) Representing data y1，zGlobal similarity coefficients in set Y;
when in use
Figure BDA0002923805680000046
Then, the candidate data l is determinedK(2) As class set C1The candidate data lK(2) Adding to class collections C1And the candidate data lK(2) Deleted in the set L (Y) when G (l)K(2)，C1) 0 or
Figure BDA0002923805680000047
Then the candidate data lK(2) Reserved in the set L (Y);
screening in set L (Y) for class set C when screening for the second time1Continuing to perform a third screening in the set L (Y) according to the method in the step (2) until the class set C is not screened in the set L (Y) at the current screening times1Stopping the next screening in the set L (Y);
continuously selecting the candidate data with the maximum global similarity coefficient in the current set L (Y) as a second class center, and marking the second class center as c2Said class center c2The class set is marked as C2Centering the class c2Deleted in the set L (Y), and screened out from the current set L (Y) by adopting the steps2Other candidate data of (2); after the screening is completed, class set C2The candidate data in (a) are deleted in the current set l (y);
repeating the above method until the number of the remaining candidate data in the current set l (y) is 0, namely completing the selection of the class center in the vital sign big data, and completing the preliminary clustering of the vital sign big data.
Preferably, the data in the vital sign big data are clustered according to the selected class center, and the noise data in the vital sign big data are removed in the clustering process, specifically:
clustering the rest non-clustered data in the set Y according to the selected class center and the primary clustering result, setting D (Y) to represent the set formed by the non-clustered data in the set Y, daRepresents the a-th non-clustered data in the set D (Y), N (d)a) Presentation setDistance non-clustering data d in YaThe nearest neighborhood set of M data defines h (d)a) Representing unclustered data daThe cluster priorities in the sets D (Y), and
Figure BDA0002923805680000051
wherein, m (d)a) Representing a neighborhood set N (d)a) The number of clustered data in, s (d)a) Representing unclustered data daGlobal similarity coefficients in set Y;
preferentially clustering the non-clustered data with the maximum clustering priority in the sets D (Y) at the moment, and setting deRepresents the e-th non-clustered data in the set D (Y), and
Figure BDA0002923805680000052
N(de) Representing distance-uncolustered data d in set YeNeighborhood set of M nearest data, M (d)e) A set of representations N (d)e) The number of the clustered data;
when m (d)e) When the value is 0, judging that the non-clustered data in the set D (Y) are all noise data, and deleting the noise data from the set D (Y);
when m (d)e) When not equal to 0, set Je，pA set of representations N (d)e) The p-th clustered data in (1), the clustered data Je，pThe class set in which is denoted Ce，pDefinition of J (d)e，Ce，p) As non-clustered data deAnd class set Ce，pThe distribution of the coefficients, then J (d)e，Ce，p) The calculation formula of (2) is as follows:
Figure BDA0002923805680000053
in the formula, Me，pA set of representations N (d)e) In the presence of a member belonging to class set Ce，pN' (J) of the clustered datae，p) Representing class set Ce，pIntermediate distance clustered data Je，pMost recent MSet of clustered data, Je，p，qThe set of representations N' (J)e，p) The q-th clustered data of (1), Je，vA set of representations N (d)e) And J to the v-th clustered data in (1)e，vAs class set Ce，pData in (1), N' (J)e，v) Representing class set Ce，pIntermediate distance clustered data Je，vSet of recent M clustered data, Je，v，bThe set of representations N' (J)e，v) The b-th clustered data in (a);
let M (d)e) A set of representations N (d)e) The number of different class sets in which the clustered data is located, Ce，nRepresenting data deAnd said M (d)e) Class sets with the smallest distribution of detection coefficients between the class sets, i.e. with the smallest distribution of detection coefficients
Figure BDA0002923805680000054
When data d is not clusteredeAnd class set Ce，nSatisfies the following conditions:
Figure BDA0002923805680000061
then the non-clustered data deAdding to class collections Ce，nAnd non-clustered data deDeleting in the set D (Y), determining the non-clustered data deAs non-noisy data, when data d is not clusteredeAnd class set Ce，nSatisfies the following conditions:
Figure BDA0002923805680000062
Figure BDA0002923805680000063
then, the non-clustered data d is determinedeFor noisy data, non-clustered data deDeleted in the set D (Y), where s (d)e) Representing unclustered data deGlobal similarity coefficient in set Y, Ye，n，rRepresenting class set Ce，nOf (1), s (y)e，n，r) Representing data ye，n，rGlobal similarity in set YCoefficient ρ (y)e，n，r，de) Representing data ye，n，rCompared with non-clustered data deA distance weighted value of, and
Figure BDA0002923805680000064
and selecting the data with the maximum clustering priority from the current set D (Y) again according to the method for carrying out priority clustering, and stopping clustering until the number of the non-clustered data in the set D (Y) is 0.
The beneficial effects created by the invention are as follows: the health assessment model for assessing the physical state of the patient according to the vital sign data of the patient is established according to the vital sign big data, so that the physical state of the patient is monitored remotely and uniformly, the workload of medical workers is reduced, the physical state of the patient can be found in time when the physical state of the patient is dangerous, and the rescue efficiency is improved; the big data processing unit is adopted to process big vital sign data, the processed big vital sign data is utilized to train the support vector machine, so that a health assessment model for assessing the physical state of a patient according to the big vital sign data is established, the big vital sign data is clustered before the big vital sign data is utilized to train the support vector machine, and noise data in the big vital sign data is removed in the clustering process, so that the influence of the noise data on the assessment accuracy of the support vector machine is avoided, and the clustered class set is used as an input value of the training support vector machine, so that the time required by training can be obviously reduced, and the performance of the support vector is improved; in the clustering process of vital sign big data, a new class center selection mode is provided, the selection of class centers of different density classes and different size classes can be adapted, and the selection precision of the class centers is high; the method for clustering the vital sign big data according to the selected class center is provided, so that the influence of noise data on a clustering result can be avoided while the big data are effectively clustered, and the clustering result has higher accuracy.
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The invention is further described with the aid of the accompanying drawings, in which, however, the embodiments do not constitute any limitation to the invention, and for a person skilled in the art, without inventive effort, further drawings may be derived from the following figures.
FIG. 1 is a schematic diagram of the present invention.
Detailed Description
The invention is further described with reference to the following examples.
Referring to fig. 1, the remote internet big data smart medical system based on the blockchain in the embodiment includes a vital sign acquisition module, a medical big data acquisition module, a blockchain storage module and a smart medical terminal, wherein the vital sign acquisition module is bound with a bed number of a patient and used for acquiring vital sign data of the patient and transmitting the acquired vital sign data and the bed number of the patient to the smart medical terminal through the internet, the medical big data acquisition module is used for collecting the vital sign big data and transmitting the collected vital sign big data to the blockchain storage module for storage, the smart medical terminal includes a big data processing unit, a vital sign analysis unit, an intelligent early warning unit, a patient information recording unit and a human-computer interaction unit, and the smart medical terminal retrieves the vital sign big data from the blockchain storage module, the vital sign analysis unit establishes a health assessment model for assessing the body state of the patient according to the vital sign data according to the processed vital sign big data, the received vital sign data of the patient is input into the health assessment model, so that whether the body state of the patient is healthy or dangerous is assessed, the intelligent early warning unit is used for giving an early warning when the body state of the patient is assessed to be dangerous, the patient information recording unit is used for recording the basic information of the patient, the vital sign data of the patient and the body state of the patient obtained by assessment, and medical staff can inquire the vital sign data of the patient and the body state of the patient by inputting the basic information of the patient into the man-machine interaction unit.
Preferably, the vital sign analysis unit adopts a support vector machine to establish a health assessment model for assessing the body state according to the vital sign data, and adopts the vital sign big data processed by the big data processing unit as a sample set for training and testing the support vector machine.
Preferably, the basic information of the patient includes a name, an age, and a bed number of the patient.
This preferred embodiment provides a long-range wisdom medical system, establishes the health assessment model that assesses according to patient's vital sign data to patient's health according to vital sign big data, has realized long-range unified guardianship to patient's health to medical personnel's work load has been alleviateed, and can in time discover when patient's health is in danger, thereby has improved efficiency of suing and labouring.
Preferably, the big data processing unit is configured to cluster the vital sign big data, remove noise data in the vital sign big data in a clustering process, determine labels of body states corresponding to various class sets obtained by clustering the vital sign big data, and in a training process of the support vector machine, use the class set of the vital sign big data as an input value of the support vector machine, and use the label of the body state corresponding to the class set as an output value of the support vector machine.
Preferably, the physical status label includes health and risk.
In the preferred embodiment, the big data processing unit is adopted to process big vital sign data, and the processed big vital sign data is utilized to train the support vector machine, so that a health assessment model for assessing the physical state of a patient according to the big vital sign data is established.
Preferably, the big data processing unit is configured to cluster the vital sign big data and remove noise data in the vital sign big data in a clustering process, and specifically includes:
(1) selecting a class center from the vital sign big data;
(2) and clustering the data in the vital sign big data according to the selected class center, and removing noise data in the vital sign big data in the clustering process.
Preferably, the class center is selected from the vital sign big data in the following way:
let Y denote the set of vital sign big data, YiRepresents the ith data in set Y, defines s (Y)i) Representing data yiGlobal similarity coefficient in set Y, and
Figure BDA0002923805680000081
wherein, yjRepresents the jth data in the set Y, and M (Y) represents the number of data in the set Y; is provided with U (y)i) Representing data yiGiven a positive integer M, the value of M may take 5, and M data in set Y are selected to be added to set U (Y) in the following manneri) The method comprises the following steps:
let yi(1) Representing distance data Y in a set YiFirst near data, ω (y)i(1) Represents data y)i(1) And ω (y) ofi(1) Is based on data
Figure BDA0002923805680000082
Centered at | yi(1)-yiL is a square region with side length, and data yi(1) Join to set U (y)i) And in the local region ω (y)i(1) Determine distance data y from the extrinsic dataiThe first most recent data is denoted as yi(2) Let ω (y)i(2) Represents data y)i(2) And ω (y) ofi(2) Is based on data
Figure BDA0002923805680000083
Centered at | yi(2)-yiL is a square region with side length, and data yi(2) Join to set U (y)i) In the local region ω (y)i(1) And a local region ω (y)i(2) Determine distance data y from the extrinsic dataiThe first most recent data is denoted as yi(3) Let ω (y)i(3) Represents data y)i(3) And ω (y) ofi(3) Is based on data
Figure BDA0002923805680000084
Centered at | yi(3)-yiL is a square region with side length, and data yi(3) Join to set U (y)i) And continuing to determine the addition of data to the set U (y) as described abovei) In, up to set U (y)i) When the number of data in the set is equal to M, stopping the direction to the set U (y)i) Adding data;
candidate data which can be used as class centers are screened out from the set Y by adopting the following formula:
Figure BDA0002923805680000091
wherein f (y)i) Representing data yiClass-centric attribute value in set Y, Yi(l) A set of representations U (y)i) The first data in (1), s (y)i(l) Represents data y)i(l) Global similarity coefficient in set Y, ρ (Y)i(l)，yi) Representing data yi(l) Compare to data yiA distance weighted value of, and
Figure BDA0002923805680000092
when data yiClass center attribute value of
Figure BDA0002923805680000093
Then data y is addediCandidates as class centersData, and judging the candidate data of the center class as non-noise data; when data yiClass center attribute value of
Figure BDA0002923805680000094
Then data y is addediThe data are regarded as non-clustered data;
setting L (Y) to represent a set formed by candidate data regarded as class centers in the set Y, selecting the class centers in the set L (Y), and clustering the data in the set L (Y) according to the selected class centers, wherein the method specifically comprises the following steps:
selecting the candidate data with the maximum global similarity coefficient in the set L (Y) as a first class center, and marking the first class center as c1Class center c1The class set is marked as C1Centering the class c1Deleted in the set L (Y), and screened out from the current set L (Y) as belonging to the class set C by the following steps1The candidate data in (1) are specifically:
step (1): let lk(1) The k-th candidate data in the set L (Y) at the time of the 1 st screening is shown, and G (l) is definedk(1)，C1) Represents the candidate data lk(1) And class set C1A clustering function between, and G (l)k(1)，C1) The expression of (a) is:
G(lk(1)，C1)＝θ(lk(1)，C1)*|s(c1)-s(lk(1))|
in the formula, theta (l)k(1)，C1) Representing a judgment function, set N (l)k(1) ) represents the distance candidates l selected in the set Yk(1) Neighborhood set of M nearest data, M (l)k(1)，C1) Representing a neighborhood set N (l)k(1) In) to the class set C1Number of data of (1), when m (l)k(1)，C1) Not equal to 0, θ (l)k(1)，C1) When m (l) is equal to 1k(1)，C1) When equal to 0, θ (l)k(1)，C1)＝0，s(c1) Representing class center c1Global similarity coefficient in set Y, s (l)k(1) Represents candidate data lk(1) Global similarity coefficients in set Y;
when in use
Figure BDA0002923805680000095
Then, the candidate data l is determinedk(1) As class set C1The candidate data lk(1) Adding to class collections C1And the candidate data lk(1) Deleted in the set L (Y) when G (l)k(1)，C1) 0 or
Figure BDA0002923805680000096
Then the candidate data lk(1) Reserved in the set L (Y);
step (2) settingK(2) Representing the Kth candidate data in the current set L (Y) at the 2 nd screening, and defining G (l)K(2)，C1) Represents the candidate data lK(2) And class set C1A clustering function between, and G (l)K(2)，C1) The expression of (a) is:
Figure BDA0002923805680000101
wherein, θ (l)K(2)，C1) Representing a judgment function, set N (l)K(2) Means for selecting distance candidates l from the set YK(2) Neighborhood set of M nearest data, M (l)K(2)，C1) Representing a neighborhood set N (l)K(2) In) to the class set C1Number of data of (1), when m (l)K(2)，C1) Not equal to 0, θ (l)K(2)，C1) When m (l) is equal to 1K(2)，C1) When equal to 0, θ (l)K(2)，C1)＝0，s(lK(2) Represents candidate data lK(2) Global similarity coefficient in set Y, Y1，zRepresenting class set C1Is the z-th data in (1), ρ (y)1，z，lK(2) Represents data y)1，zCompared with the candidate data lK(2) A distance weighted value of, and
Figure BDA0002923805680000102
Figure BDA0002923805680000103
s(y1，z) Representing data y1，zGlobal similarity coefficients in set Y;
when in use
Figure BDA0002923805680000104
Then, the candidate data l is determinedK(2) As class set C1The candidate data lK(2) Adding to class collections C1And the candidate data lK(2) Deleted in the set L (Y) when G (l)K(2)，C1) 0 or
Figure BDA0002923805680000105
Then the candidate data lK(2) Reserved in the set L (Y);
screening in set L (Y) for class set C when screening for the second time1Continuing to perform a third screening in the set L (Y) according to the method in the step (2) until the class set C is not screened in the set L (Y) at the current screening times1Stopping the next screening in the set L (Y);
continuously selecting the candidate data with the maximum global similarity coefficient in the current set L (Y) as a second class center, and marking the second class center as c2Said class center c2The class set is marked as C2Centering the class c2Deleted in the set L (Y), and screened out from the current set L (Y) by adopting the steps2The candidate data of (1); after the screening is completed, class set C2The candidate data in (a) are deleted in the current set l (y);
repeating the above method until the number of the remaining candidate data in the current set l (y) is 0, namely completing the selection of the class center in the vital sign big data, and completing the preliminary clustering of the vital sign big data.
The preferred embodiment is used for selecting the class center from the vital sign big data, so that the vital sign big data are clustered according to the selected class center. When big data is clustered, the selection of the class center directly influences the accuracy of a late clustering result and the clustering efficiency and also determines the accuracy of noise data detection; most of the traditional selection modes of the class centers are easily influenced by class density and class size, so that high-density classes and class centers with smaller-size classes are easily selected, class centers with low-density classes or class centers with larger-size classes are ignored, and the final clustering effect is influenced, aiming at the phenomenon, the mode of screening candidate data which can be used as the class centers from vital sign big data provided by the preferred embodiment can effectively screen the class centers with different densities and different sizes, namely the screening mode of the class centers is not influenced by the class density and the class size, the detection precision of the class centers with the same size for the low-density classes or the classes with larger sizes is realized, because the screening mode of the class centers of the preferred embodiment calculates the absolute difference between the global similarity coefficient of the data and the weighted average value of the global similarity coefficient of the neighborhood data, the global similarity coefficient of the data can effectively measure the distribution characteristics of the data in vital sign big data, the selection mode of the neighborhood data in the neighborhood data set can ensure the centrality of the data in the selected neighborhood data, the phenomenon that the selected neighborhood data is positioned at one side of the data is avoided, the central attribute of the data is measured according to the absolute difference value between the global similarity coefficient of the data and the weighted mean value of the global similarity coefficient of the neighborhood data, when the data is positioned near a class center or class center, the global similarity coefficient and the global similarity coefficient of the selected neighborhood data have larger similarity no matter what density or size the data is positioned, therefore, the central attribute of the data can be effectively judged by calculating the similarity of the global similarity coefficient between the data and the neighborhood data, the method is not influenced by class density or class size, so that the detection precision of class centers of classes with smaller density or larger size is improved; according to the method, data in the vital sign big data and in the class center or near the class center can be effectively screened, the class center selection mode provided by the preferred embodiment is continuously adopted, the class center can be effectively selected, meanwhile, the data near the class center is clustered into the corresponding class set, namely, the preliminary clustering of the vital sign big data is completed, and a foundation is laid for the subsequent clustering and noise detection.
Preferably, the data in the vital sign big data are clustered according to the selected class center, and the noise data in the vital sign big data are removed in the clustering process, specifically:
clustering the rest non-clustered data in the set Y according to the selected class center and the primary clustering result, setting D (Y) to represent the set formed by the non-clustered data in the set Y, daRepresents the a-th non-clustered data in the set D (Y), N (d)a) Representing distance-uncolustered data d in set YaThe nearest neighborhood set of M data defines h (d)a) Representing unclustered data daThe cluster priorities in the sets D (Y), and
Figure BDA0002923805680000111
wherein, m (d)a) Representing a neighborhood set N (d)a) The number of clustered data in, s (d)a) Representing unclustered data daGlobal similarity coefficients in set Y;
preferentially clustering the non-clustered data with the maximum clustering priority in the sets D (Y) at the moment, and setting deRepresents the e-th non-clustered data in the set D (Y), and
Figure BDA0002923805680000112
N(de) Representing distance-uncolustered data d in set YeNeighborhood set of M nearest data, M (d)e) A set of representations N (d)e) The number of the clustered data;
when m (d)e) When the value is 0, judging that the non-clustered data in the set D (Y) are all noise data, and deleting the noise data from the set D (Y);
when m (d)e) When not equal to 0, set Je，pA set of representations N (d)e) The p-th clustered data in (1), the clustered data Je，pThe class set in which is denoted Ce，pDefinition of J (d)e，Ce，p) As non-clustered data deAnd class set Ce，pThe distribution of the coefficients, then J (d)e，Ce，p) The calculation formula of (2) is as follows:
Figure BDA0002923805680000121
in the formula, Me，pA set of representations N (d)e) In the presence of a member belonging to class set Ce，pN' (J) of the clustered datae，p) Representing class set Ce，pIntermediate distance clustered data Je，pSet of recent M clustered data, Je，p，qThe set of representations N' (J)e，p) The q-th clustered data of (1), Je，vA set of representations N (d)e) And J to the v-th clustered data in (1)e，vAs class set Ce，pData in (1), N' (J)e，v) Representing class set Ce，pIntermediate distance clustered data Je，vSet of recent M clustered data, Je，v，bThe set of representations N' (J)e，v) The b-th clustered data in (a);
let M (d)e) A set of representations N (d)e) The number of different class sets in which the clustered data is located, Ce，nRepresenting data deAnd said M (d)e) Class sets with the smallest distribution of detection coefficients between the class sets, i.e. with the smallest distribution of detection coefficients
Figure BDA0002923805680000122
When data d is not clusteredeAnd class set Ce，nSatisfies the following conditions:
Figure BDA0002923805680000123
then the non-clustered data deAdd to classSet Ce，nAnd non-clustered data deDeleting in the set D (Y), determining the non-clustered data deAs non-noisy data, when data d is not clusteredeAnd class set Ce，nSatisfies the following conditions:
Figure BDA0002923805680000124
Figure BDA0002923805680000125
then, the non-clustered data d is determinedeFor noisy data, non-clustered data deDeleted in the set D (Y), where s (d)e) Representing unclustered data deGlobal similarity coefficient in set Y, Ye，n，rRepresenting class set Ce，nOf (1), s (y)e，n，r) Representing data ye，n，rGlobal similarity coefficient in set Y, ρ (Y)e，n，r，de) Representing data ye，n，rCompared with non-clustered data deA distance weighted value of, and
Figure BDA0002923805680000126
and selecting the data with the maximum clustering priority from the current set D (Y) again according to the method for carrying out priority clustering, and stopping clustering until the number of the non-clustered data in the set D (Y) is 0.
The preferred embodiment is used for clustering the non-clustered data in the vital sign big data according to the selected class center and the preliminary clustering result, removing the noise data in the vital sign big data, defining the clustering priority for the non-clustered data, wherein the clustering priority comprehensively considers the global similarity coefficient of the non-clustered data and the number of the clustered data in the neighborhood set, when the non-clustered data has a larger global similarity coefficient and the neighborhood set has more clustered data, the non-clustered data has a higher probability of being the data in the class set, so that the non-clustered data with the maximum clustering priority is selected from the non-clustered data set for clustering in an iterative mode, the non-noise data in the non-clustered data can be guaranteed to be preferentially clustered, and a foundation is laid for the clustering of the next non-clustered data, the influence of noise data on the clustering result can be avoided; when clustering is carried out on the non-clustered data with the maximum clustering priority, when the maximum clustering priority is 0 at the moment, the remaining non-clustered data in the vital sign big data are judged to be noise data, when the maximum clustering priority is not 0 at the moment, a distribution detection coefficient is defined for measuring the distribution similarity between the non-clustered data and a class set in which the clustered data are located in a neighborhood set, the distribution detection coefficient is defined for measuring the distribution characteristic of the data in the class set to be detected by calculating the average distance between the clustered data in the neighborhood set and M pieces of closer clustered data in the class set in which the clustered data are located, the distribution characteristic between the data and the data in the class set to be detected is measured by calculating the distance between the non-clustered data and the clustered data in the neighborhood set, and finally the distribution characteristics between the non-clustered data and the clustered data in the class set to be detected are compared, the class set which is most similar to the distribution characteristic of the non-clustered data has the class set with the maximum probability of the non-clustered data belonging thereto, so that the class set with the minimum distribution detection coefficient value between the non-clustered data and the non-clustered data is selected for detection, and in the detection process, whether the non-clustered data is the data of the class set or not is judged by comparing the similarity between the global similarity coefficients of the non-clustered data and the data in the class set to be detected, so that the non-clustered data can be effectively clustered, and the noise data in the non-clustered data can be effectively detected.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims (8)

1. The remote Internet big-data intelligent medical system based on the blockchain is characterized by comprising a vital sign acquisition module, a medical big-data acquisition module, a blockchain storage module and an intelligent medical terminal, wherein the vital sign acquisition module is bound with a bed number of a patient and used for acquiring vital sign data of the patient and transmitting the acquired vital sign data and the bed number of the patient to the intelligent medical terminal through the Internet, the medical big-data acquisition module is used for collecting the vital sign big data and transmitting the collected vital sign big data to the blockchain storage module for storage, the intelligent medical terminal comprises a big-data processing unit, a vital sign analysis unit, an intelligent early warning unit, a patient information recording unit and a human-computer interaction unit, and the intelligent medical terminal retrieves the vital sign big data from the blockchain storage module, the vital sign analysis unit establishes a health assessment model for assessing the body state of the patient according to the vital sign data according to the processed vital sign big data, the received vital sign data of the patient is input into the health assessment model, so that whether the body state of the patient is healthy or dangerous is assessed, the intelligent early warning unit is used for giving an early warning when the body state of the patient is assessed to be dangerous, the patient information recording unit is used for recording the basic information of the patient, the vital sign data of the patient and the body state of the patient obtained by assessment, and medical staff can inquire the vital sign data of the patient and the body state of the patient by inputting the basic information of the patient into the man-machine interaction unit.
2. The remote internet big data intelligent medical system based on the blockchain as claimed in claim 1, wherein the vital sign analysis unit uses a support vector machine to establish a health assessment model for body state assessment according to the vital sign data, and uses the vital sign big data processed by the big data processing unit as a sample set for training and testing the support vector machine.
3. The remote internet big data intelligent medical system based on the blockchain as claimed in claim 2, wherein the basic information of the patient includes a name, an age and a bed number of the patient.
4. The remote internet big data intelligent medical system based on the block chain as claimed in claim 3, wherein the big data processing unit is configured to cluster the big vital sign data, remove noise data in the big vital sign data during the clustering process, determine labels of body states corresponding to each class set obtained by clustering the big vital sign data, and during the training process of the support vector machine, use the class set of the big vital sign data as an input value of the support vector machine, and use the labels of body states corresponding to the class set as an output value of the support vector machine.
5. The blockchain-based remote internet big data intelligent medical system according to claim 4, wherein the label of the physical status includes health and danger.
6. The remote internet big data intelligent medical system based on the blockchain as claimed in claim 5, wherein the big data processing unit is used for clustering the big vital sign data and removing noise data in the big vital sign data in the clustering process, and specifically comprises:
(1) selecting a class center from the vital sign big data;
(2) and clustering the data in the vital sign big data according to the selected class center, and removing noise data in the vital sign big data in the clustering process.
7. The intelligent medical system of remote internet big data based on blockchain as claimed in claim 6, wherein the following method is adopted to select the class center from the vital sign big data:
let Y denote the set of vital sign big data, YiRepresents the ith data in set Y, defines s (Y)i) Representing data yiGlobal similarity coefficient in set Y, and
Figure FDA0002923805670000021
wherein, yjRepresents the jth data in the set Y, and M (Y) represents the number of data in the set Y; is provided with U (y)i) Representing data yiGiven a positive integer M, M data are selected from the set Y and added into the set U (Y)i) The method comprises the following steps:
let yi(1) Representing distance data Y in a set YiFirst near data, ω (y)i(1) Represents data y)i(1) And ω (y) ofi(1) Is based on data
Figure FDA0002923805670000022
Centered at | yi(1)-yiL is a square region with side length, and data yi(1) Join to set U (y)i) And in the local region ω (y)i(1) Determine distance data y from the extrinsic dataiThe first most recent data is denoted as yi(2) Let ω (y)i(2) Represents data y)i(2) And ω (y) ofi(2) Is based on data
Figure FDA0002923805670000023
Centered at | yi(2)-yiL is a square region with side length, and data yi(2) Join to set U (y)i) In the local region ω (y)i(1) And a local region ω (y)i(2) Determine distance data y from the extrinsic dataiThe first most recent data is denoted as yi(3) Let ω (y)i(3) Represents data y)i(3) And ω (y) ofi(3) Is based on data
Figure FDA0002923805670000024
Centered at | yi(3)-yiL is a square region with side length, and data yi(3) Join to set U (y)i) And continuing to determine the addition of data to the set U (y) as described abovei) In (1) to (1)And U (y)i) When the number of data in the set is equal to M, stopping the direction to the set U (y)i) Adding data;
candidate data which can be used as class centers are screened out from the set Y by adopting the following formula:
Figure FDA0002923805670000025
wherein f (y)i) Representing data yiClass-centric attribute value in set Y, Yi(l) A set of representations U (y)i) The first data in (1), s (y)i(l) Represents data y)i(l) Global similarity coefficient in set Y, ρ (Y)i(l)，yi) Representing data yi(l) Compare to data yiA distance weighted value of, and
Figure FDA0002923805670000026
when data yiClass center attribute value of
Figure FDA0002923805670000027
Then data y is addediCandidate data regarded as class center, and the data y is determinediIs non-noise data; when data yiClass center attribute value of
Figure FDA0002923805670000031
Then data y is addediThe data are regarded as non-clustered data;
setting L (Y) to represent a set formed by candidate data regarded as class centers in the set Y, selecting the class centers in the set L (Y), and clustering the candidate data in the set L (Y) according to the selected class centers, wherein the method specifically comprises the following steps:
selecting the candidate data with the maximum global similarity coefficient in the set L (Y) as a first class center, and marking the first class center as c1Class center c1The class set is marked as C1Centering the class c1Deleted in the sets L (Y) and collectedThe class set C is screened from the current set L (Y) by the following steps1The other candidate data of (2) are specifically:
step (1): let lk(1) The k-th candidate data in the set L (Y) at the time of the 1 st screening is shown, and G (l) is definedk(1)，C1) Represents the candidate data lk(1) And class set C1A clustering function between, and G (l)k(1)，C1) The expression of (a) is:
G(lk(1)，C1)＝θ(lk(1)，C1)*|s(lk(1))-s(c1)|
in the formula, theta (l)k(1)，C1) Representing a judgment function, set N (l)k(1) ) represents the distance candidates l selected in the set Yk(1) Neighborhood set of M nearest data, M (l)k(1)，C1) Representing a neighborhood set N (l)k(1) In) to the class set C1Number of data of (1), when m (l)k(1)，C1) Not equal to 0, θ (l)k(1)，C1) When m (l) is equal to 1k(1)，C1) When equal to 0, θ (l)k(1)，C1)＝0，s(c1) Representing class center c1Global similarity coefficient in set Y, s (l)k(1) Represents candidate data lk(1) Global similarity coefficients in set Y;
when in use
Figure FDA0002923805670000032
Then, the candidate data l is determinedk(1) As class set C1The candidate data lk(1) Adding to class collections C1And the candidate data lk(1) Deleted in the set L (Y) when G (l)k(1)，C1) 0 or
Figure FDA0002923805670000033
Then the candidate data lk(1) Reserved in the set L (Y);
step (2) settingK(2) Represents the Kth candidate data in the current set L (Y) at the 2 nd screeningDefinition of G (l)K(2)，C1) Represents the candidate data lK(2) And class set C1A clustering function between, and G (l)K(2)，C1) The expression of (a) is:
Figure FDA0002923805670000034
wherein, θ (l)K(2)，C1) Representing a judgment function, set N (l)K(2) Means for selecting distance candidates l from the set YK(2) Neighborhood set of M nearest data, M (l)K(2)，C1) Representing a neighborhood set N (l)K(2) In) to the class set C1Number of data of (1), when m (l)K(2)，C1) Not equal to 0, θ (l)K(2)，C1) When m (l) is equal to 1K(2)，C1) When equal to 0, θ (l)K(2)，C1)＝0，s(lK(2) Represents candidate data lK(2) Global similarity coefficient in set Y, Y1，zRepresenting class set C1Is the z-th data in (1), ρ (y)1，z，lK(2) Represents data y)1，zCompared with the candidate data lK(2) A distance weighted value of, and
Figure FDA0002923805670000035
Figure FDA0002923805670000041
s(y1，z) Representing data y1，zGlobal similarity coefficients in set Y;
when in use
Figure FDA0002923805670000042
Then, the candidate data l is determinedK(2) As class set C1The candidate data lK(2) Adding to class collections C1And the candidate data lK(2) Deleted in the set L (Y) when G (l)K(2)，C1) 0 or
Figure FDA0002923805670000043
Then the candidate data lK(2) Reserved in the set L (Y);
screening in set L (Y) for class set C when screening for the second time1Continuing to perform a third screening in the set L (Y) according to the method in the step (2) until the class set C is not screened in the set L (Y) at the current screening times1Stopping the next screening in the set L (Y);
continuously selecting the candidate data with the maximum global similarity coefficient in the current set L (y) as a second class center, and marking the second class center as c2Said class center c2The class set is marked as C2Centering the class c2Deleted in the set L (Y), and screened out from the current set L (Y) by adopting the steps2Other candidate data of (2); after the screening is completed, class set C2The candidate data in (a) are deleted in the current set l (y);
repeating the above method until the number of the remaining candidate data in the current set l (y) is 0, namely completing the selection of the class center in the vital sign big data, and completing the preliminary clustering of the vital sign big data.
8. The remote internet big data intelligent medical system based on the blockchain as claimed in claim 7, wherein the data in the vital sign big data are clustered according to the selected class center, and the noise data in the vital sign big data are removed in the clustering process, specifically:
clustering the rest non-clustered data in the set Y according to the selected class center and the primary clustering result, setting D (Y) to represent the set formed by the non-clustered data in the set Y, daRepresents the a-th non-clustered data in the set D (Y), N (d)a) Representing distance-uncolustered data d in set YaThe nearest neighborhood set of M data defines h (d)a) Representing unclustered data daIn the collectionAnd the cluster priorities in D (Y), and
Figure FDA0002923805670000044
wherein, m (d)a) Representing a neighborhood set N (d)a) The number of clustered data in, s (d)a) Representing unclustered data daGlobal similarity coefficients in set Y;
preferentially clustering the non-clustered data with the maximum clustering priority in the sets D (Y) at the moment, and setting deRepresents the e-th non-clustered data in the set D (Y), and
Figure FDA0002923805670000045
N(de) Representing distance-uncolustered data d in set YeNeighborhood set of M nearest data, M (d)e) A set of representations N (d)e) The number of the clustered data;
when m (d)e) When the value is 0, judging that the non-clustered data in the set D (Y) are all noise data, and deleting the noise data from the set D (Y);
when m (d)e) When not equal to 0, set Je，pA set of representations N (d)e) The p-th clustered data in (1), the clustered data Je，pThe class set in which is denoted Ce，pDefinition of J (d)e，Ce，p) As non-clustered data deAnd class set Ce，pThe distribution of the coefficients, then J (d)e，Ce，p) The calculation formula of (2) is as follows:
Figure FDA0002923805670000051
in the formula, Me，pA set of representations N (d)e) In the presence of a member belonging to class set Ce，pN' (J) of the clustered datae，p) Representing class set Ce，pIntermediate distance clustered data Je，pSet of recent M clustered data, Je，p，qThe set of representations N' (J)e，p) The q-th clustered number of (1)According to, Je，vA set of representations N (d)e) Is the v-th clustered data in (1), and je，vAs class set Ce，pData in (1), N' (J)e，v) Representing class set Ce，pIntermediate distance clustered data Je，vSet of recent M clustered data, Je，v，bThe set of representations N' (J)e，v) The b-th clustered data in (a);
let M (d)e) A set of representations N (d)e) The number of different class sets in which the clustered data is located, Ce，nRepresenting data deAnd said M (d)e) Class sets with the smallest distribution of detection coefficients between the class sets, i.e. with the smallest distribution of detection coefficients
Figure FDA0002923805670000052
When data d is not clusteredeAnd class set Ce，nSatisfies the following conditions:
Figure FDA0002923805670000053
then the non-clustered data deAdding to class collections Ce，nAnd non-clustered data deDeleting in the set D (Y), determining the non-clustered data deAs non-noisy data, when data d is not clusteredeAnd class set Ce，nSatisfies the following conditions:
Figure FDA0002923805670000054
Figure FDA0002923805670000055
then, the non-clustered data d is determinedeFor noisy data, non-clustered data deDeleted in the set D (Y), where s (d)e) Representing unclustered data deGlobal similarity coefficient in set Y, Ye，n，rRepresenting class set Ce，nOf (1), s (y)e，n，r) Representing data ye，n，rGlobal similarity coefficient in set Y, ρ (Y)e，n，r，de) Representing data ye，n，rCompared with non-clustered data deA distance weighted value of, and
Figure FDA0002923805670000056
and selecting the data with the maximum clustering priority from the current set D (Y) again according to the method for carrying out priority clustering, and stopping clustering until the number of the non-clustered data in the set D (Y) is 0.
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